Exploring energy efficiency in China׳s iron and steel industry: A stochastic frontier approach

Energy Policy 72 (2014) 87–96

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Energy Policy journal homepage: www.elsevier.com/locate/enpol

Exploring energy efficiency in China's iron and steel industry: A stochastic frontier approach Boqiang Lin a,b,n, Xiaolei Wang c a

School of Energy Research, Collaborative Innovation Center for Energy Economics and Energy Policy, Xiamen University, Xiamen, Fujian 361005, PR China Newhuadu Business School, Minjiang University, Fuzhou 350108, PR China c School of Energy Research, Xiamen University, Xiamen 361005, PR China b

H I G H L I G H T S








A stochastic frontier model is adopted to analyze energy efficiency. Industry concentration and ownership structure are main factors affecting the non-efficiency. Energy efficiency of China's iron and steel industry shows a fluctuating increase. Regional differences of energy efficiency are further analyzed. Future policy for energy conservation in China's iron and steel sector is suggested.

art ic l e i nf o

a b s t r a c t

Article history: Received 22 January 2014 Received in revised form 29 April 2014 Accepted 30 April 2014 Available online 27 May 2014

The iron and steel industry is one of the major energy-consuming industries in China. Given the limited research on effective energy conservation in China's industrial sectors, this paper analyzes the total factor energy efficiency and the corresponding energy conservation potential of China's iron and steel industry using the excessive energy-input stochastic frontier model. The results show that there was an increasing trend in energy efficiency between 2005 and 2011 with an average energy efficiency of 0.699 and a cumulative energy conservation potential of 723.44 million tons of coal equivalent (Mtce). We further analyze the regional differences in energy efficiency and find that energy efficiency of Northeastern China is high while that of Central and Western China is low. Therefore, there is a concentration of energy conservation potential for the iron and steel industry in the Central and Western areas. In addition, we discover that inefficient factors are important for improving energy conservation. We find that the structural defect in the economic system is an important impediment to energy efficiency and economic restructuring is the key to improving energy efficiency. & 2014 Elsevier Ltd. All rights reserved.

Keywords: Stochastic frontier Energy conservation China's iron and steel industry

1. Introduction The iron and steel industry is one of the basic industries in the process of economic development and its growth is an important indicator of the level of industrialization. China has focused on the development of the iron and steel industry since the founding of the People's Republic of China. In 1996, China's crude steel production was over 100 million tons for the first time, and has experienced a rapid growth since then. China's crude steel production had ranked first in the world consistently for 15 years.

n Corresponding author at: Newhuadu Business School, Minjiang University, Fuzhou, Fujian, 350108, PR China. Tel.: þ 86 5922186076; fax: þ 86 5922186075. E-mail address: [email protected] (B. Lin).

http://dx.doi.org/10.1016/j.enpol.2014.04.043 0301-4215/& 2014 Elsevier Ltd. All rights reserved.

The average annual growth rate of the crude steel production reached 18.5% in the first decade of the 21st century. In 2011, the output of the sector rose to 684 million tons, which accounted for 45.04% of the world's total steel production (see Fig. 1). China's crude steel production is about 8 and 6 times larger than that of the United States and Japan respectively. Consistent with the overall growth trend, export of China's iron and steel is also growing, and its structure is optimizing. China transited from a net importer of steel products to a net exporter in 2006. From 2002 to 2011, exports of semi-finished and finished steel products increased from 6642 thousand ton to 47,899 thousand ton, indicating nearly 7 times increase. As large scale infrastructure development and high urbanization rates continue in China, the iron and steel production will also maintain high yields in the coming years.

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Million Tons

However, the rapid development of the iron and steel industry has resulted in significant increase in energy consumption in the sector as shown in Table 1 and Fig. 2. In 2002, China's iron and steel industry's energy consumption was 206.4 million tons of coal equivalents (Mtce). In 2011, energy consumption increased to 588.97 Mtce, which is 2.85 times that of 2002. Compared with other sectors, energy consumption of the iron and steel sector accounted for 29.39% of total energy consumption in the manufacturing sector. In 2011, electricity consumption of the iron and steel industry was 524.83 TWh, accounting for 20.6% of the manufacturing sector total electricity consumption. The percentage of the energy consumption of the iron and steel sector to total industry rose from 18.17% in 2002 to 24% in 2011, representing an average growth of 23.04%. The proportion of energy consumption of the sector in total primary energy consumption rose from 12.95% to 16.92%, an average of 16.45% during the same period. In China, coal accounted for about 70% of primary energy consumption, while coal-fired electric power generation accounted for about 80% of electric power. The increase in energy demand has exerted enormous pressure on energy resources and has resulted in severe carbon dioxide (CO2) emissions. According to Shangguan et al. (2010), the direct CO2 emissions of China's iron and steel industry was 920 million tons in 2007. The CO2 emissions of the industry accounted for 15% of total emissions in China (Xu et al., 2013), and cause serious environmental problems. Therefore, China's iron and steel industry should be the biggest target for energy conservation and carbon emissions reduction. On October 24, 2011, the Ministry of Industry and Information Technology of China issued “the ‘12th Five-Year' development plan of the iron and steel industry” (MIIT, 2011). This plan proposes that both energy intensity (energy consumption/industrial value added) and CO2 emissions should be cut by 18% during the “12th Five-Year” period. This plan poses a new challenge for China's iron and

steel industry. Saving energy in this industry is essential for the country's energy conversation and carbon-reduction efforts. Currently, given the constraints of energy scarcity, the problem of environmental pollution, the challenge of high energy cost and the goal of carbon reduction, energy conservation will become a major strategy for China in her transition to a low-carbon economy. However, the energy sector in China has been controlled by the government for a long time, making the prices that producers and consumers pay for energy commodities lower than the actual price in the competitive market. The gap between the competitive price and the controlled price is defined as price subsidy and paid by the government. Even for those energy commodities whose pricing mechanisms are gradually becoming market-oriented, the prices do not reflect the external costs of resource utilization and environmental impacts. Price distortion makes it difficult for market to pass correct signals to producers, resulting in the excessive use of energy. Therefore, it is important to study the energy conservation potentials of China's iron and steel industry. The study on energy conservation potential is based on the accurate measurement of the energy efficiency. There are two kinds of energy efficiency: the single factor energy efficiency and the total factor energy efficiency (Shi and Chen, 2011). Many studies on energy saving such as Lin et al. (2011, 2012) and Lin and Zhang (2013) are based on the single factor energy efficiency, which is defined as energy intensity (energy consumption/Gross Domestic Product), or energy consumption/industrial gross value in a specific industry. Previous research analyzed energy intensity of the steel industry, chemical industry and nonferrous metals industry and calculated the energy or electricity conservation potential under different scenarios. However, such calculation of energy efficiency contains an implicit assumption that output is obtained with energy as the only input.

600

25%

500

20%

50%

1600 1400 1200 1000 800 600 400 200 0

400

15%

40% 30% 20% 10%

300

Production of Crude Steel (China)

5%

100 0

0% 2002 2003 2004 2005 2006 2007

10%

200

0% 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

2008 2009 2010 2011

Electricity consumption (Twh)

Production of Crude steel (World)

Proportion of electricity consumption in Manufacturing (%)

Proportion of China's Crude steel

Fig. 1. Production of crude steel. Source: The data come from World Steel Association (2012).

Fig. 2. Electricity consumption of China's iron and steel industry. Source: The data come from China Energy Statistical Yearbook (2012).

Table 1 Energy consumption of China's iron and steel industry. Source: The data come from China Energy Statistical Yearbook (2012). Year

Iron and steel sector (Mtce)

Manufacturing (Mtce)

Proportion: Iron and steel sector/manufacturing (%)

Industry (Mtce)

Proportion: Iron and steel sector/industry (%)

Total consumption in China (Mtce)

Proportion: Iron and steel sector/total consumption(%)

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

206.4 259.42 313.99 395.44 447.3 501.87 518.63 564.04 575.34 588.97

881.8 1028.4 1230 1371.4 1512.7 1649.5 1721.1 1806 1894.1 2004

23.41 25.23 25.53 28.83 29.57 30.43 30.13 31.23 30.38 29.39

1136 1311.7 1525.1 1687.2 1849.5 2005.3 2093 2192 2320.2 2464.4

18.17 19.78 20.59 23.44 24.18 25.03 24.78 25.73 24.80 23.90

1594.3 1837.9 2134.6 2360 2586.8 2805.1 2914.5 3066.5 3249.4 3480

12.95 14.12 14.71 16.76 17.29 17.89 17.79 18.39 17.71 16.92

B. Lin, X. Wang / Energy Policy 72 (2014) 87–96

To summarize, such method just measures a proportional relationship between the energy input and output. As a measurement of energy efficiency, this method has significant limitation (Hu and Wang, 2006). In fact, the total factor energy efficiency can better reflect the reality of production, and energy efficiency also depend on improvements in the total factor productivity (Hu and Wang, 2006; Gale, 2000). In this paper, we use the inter-provincial panel data to measure the total factor energy efficiency of China's iron and steel industry. Based on the estimation of the energy efficiency, we further analyze the energy conservation potential of the iron and steel industry. The remaining part of this paper is organized as follows. Section 2 reviews the existing studies on the total factor energy efficiency. Section 3 describes the excessive energy-input stochastic frontier model used in this paper. Section 4 outlines the main variables used in this paper and the relevant data. Section 5 offers the empirical analysis and results of our model. We provide the final conclusions and policy proposals in Section 6.

2. Literature review There are mainly two categories of the frontier analysis for energy efficiency. The first is Data Envelopment Analysis (DEA), and the second is parameterized Stochastic Frontier Analysis (SFA). The basic idea of both methods is the same. First, an effective efficiency frontier should be estimated, and then the efficiency is calculated as the relative distance between the actual output or input and the frontier. The choice of the two methods hinges on the concerned issues. The DEA method is a nonparametric mathematical programming approach which does not require the sum of different outputs. As a result, this method can avoid model misspecification. There are some literatures on energy efficiency and energy saving potential in the industrial sector that use the DEA method. Azadeh et al. (2007) integrated DEA and Principal Component Analysis (PCA) method to analyze the energy efficiency in the energyintensive industries of main OECD countries. The results showed that the energy conservation potential of fossil fuel is larger than that of electricity. Shi et al. (2010) analyzed the energy efficiency in China's industry using provincial panel data. The results showed that the excessive dependence on energy is the main reason for energy wastage. Hernández-Sancho et al. (2011) analyzed the energy efficiency in Spain's waste water treatment industry. The results showed that energy efficiency in this industry was very low (only 10%); and enterprise scale and the quantity of organic matter removal were the main factors explaining efficiency. Similarly, Olanrewaju et al. (2012) studied energy consumption in 15 Canadian industries using index decomposition analysis (IDA), artificial neural network (ANN) and DEA method. By using DEA and ANN together, this paper estimates the energy efficiency and predicts the future value. Toshiyuki and Mika (2012) scored and ranked the Japan's power plants according to their energy efficiency, and found that the operating performance of Japan's power plants was almost unchanged between 2005 and 2009. Using the same method, Benyamin et al. (2013) estimated energy use efficiency and CO2 reduction potential in greenhouse cucumber production in Iran. Chang et al. (2013) use this method to study environment inefficiency in China's transportation sector and found that the efficiency of most provinces in China was below 50%. The CO2 reduction potential was between 1.6 million tons (Qinghai) and 33 million tons (Shanghai and Guangdong). SFA method is a parametric approach considering stochastic noises, which can discern the influence of various factors on inefficiency. This method had higher discriminating power for evaluating the energy efficiency performance (Zhou et al., 2012;

89

Lin and Du, 2013). Many researchers use SFA methods for energy efficiency analysis of specific industries. Maria et al. (2002) analyzed the energy efficiency of all kinds of Spanish industries using Cobb–Douglas SFA, and they concluded that energy regulatory policy should be implemented to reduce carbon emission. Buck and Young (2007) studied the energy use efficiency of Canadian commercial building, and the results showed that the energy efficiency was very high; the ownership of buildings and economic activities of the building are the main factors that have impact on the energy efficiency. Boyd (2008) analyzed the energy efficiency of wet corn milling plants and concluded that the advantage of the SFA method was avoiding the problem of energy intensity definition. Boyd et al. (2008) used stochastic frontier regression analysis to estimate plant-level energy use efficiency of US manufacturing sector. Shi et al. (2008) used variance decomposition method to estimate the influence of major factors on energy efficiency in China from 1980 to 2005. The results showed that the average contribution of total factor productivity, capital– energy ratio and labor–energy ratio was 36.54%, 45.67% and 17.89% respectively. Alfonso et al. (2012) analyzed the energy inefficiency of Spanish food and drink, textile, chemical and non-metallic mineral products sectors using SFA, and found that the energy conservation potential of each sector is about 20%. Massimo and Lester (2012) studied the energy consumption and energy efficiency of residential sector by using the data of 48 states of the United States during 1995–2007. Energy intensity did not adequately represent energy efficiency. Through the control of a series of economic variables, energy efficiency can be measured using SFA model. Based on stochastic frontier production function, Lin and Yang (2013) estimated the energy efficiency and energy conservation potential of China's thermal power industry. They found that the average energy efficiency during 2005–2010 was 0.85 and cumulative energy conservation potential was 551.04 Mtce. Studies on the total factor energy efficiency in China's iron and steel industry are scarce. Shi and Chen (2011) evaluated the total factor energy efficiency during 1992–2008 based on the DEA technique, and they concluded that there is huge potential for energy efficiency improvement in the industry. Han and Liu (2011) conducted the dynamic analysis of change of energy efficiency using Malmquist efficiency index. Zhang et al. (2012) analyzed the energy efficiency of 27 listed Chinese iron and steel companies using the same technique. Their study also concluded that the energy conservation of China's iron and steel companies are huge. From the literature, we found that the existing analysis of the total factor energy efficiency in China's steel industry is limited to the use of DEA methodology. In this paper, we use SFA method to evaluate the total factor energy efficiency of China's iron and steel industry and then provide policy proposals for energy conservation.

3. Methodology In order to analyze the total factor energy efficiency, we try to model the excessive use of energy in China's iron and steel production. In empirical analysis of demand on production factors, research generally starts with the neoclassical economic assumption of profit maximization or cost minimization to derive the demand function. This does not take into account the market failure problem. However, if the problem of externalities, price controls or inadequate market competition and other factors invalidate the above assumption about producer's behavior, estimations of factor demand functions based on the assumption tend to be biased and inconsistent (Popp and Chavas, 1994). Debreu (1951) and Farrell (1957) conducted groundbreaking research on production efficiency. The former proposed the technical efficiency measurement based on the output direction

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while the latter proposed the efficiency measurement based on the input direction. Both measurements are known collectively as Debreu–Farrel efficiency (Kumbhakar, 2000). Debreu–Farrel efficiency takes the production frontier as benchmarks. Because the measurement is convenient to use, Debreu–Farrel efficiency becomes the basic framework of production efficiency analysis. If input factors contain capital (K), labor (L) and energy (E), the input distance function can be expressed asDI ðE; K; L; YÞ ¼ supfβ : ðE=β; K; L; Y A P I g (Lin and Du, 2013). For any feasible output, DI Z 1. If the input is on the boundary, DI ¼ 1. If within the boundary, DI 4 1, which indicates that there is technical inefficiency in production. Assume that the distance function is trans-log production function. The advantage of trans-log production function is simplicity and flexibility, which also considers the interactions among variables. Coellia and Perelman (1999) established a multi-output and multi-input distance function. According to their study, under a single output and multi-input production circumstances, the distance function can be expressed as 1 1 Ln DI ¼ β0 þ βy Ln Y þ ∑ βi Ln X i þ ∑ ∑ βij Ln X i Ln X j þ βyy ðLn YÞ2 2i j 2 i 1 þ∑ βiy Ln X i Ln Y þ βt T þ βtt T 2 þ ∑ βit T Ln X i þ ∑ βty T Ln Y 2 i i i

Eq. (5) represents a stochastic frontier model about energy input. Transform Eq. (5) into: Eαit ¼ f ðY it ; K it ; Lit ; Z it ; βÞeuit þ vit

where f ð UÞis energy demand function from the technical point, the certain frontier of the energy input. Z it represents the other external factors. When output is given, this paper takes the deterministic frontier as benchmarks, defines the excess of actual energy input over the deterministic frontier as excessive input due to technical inefficiency, and defines the energy saving through the elimination of technical inefficiency as energy conservation potential. Since the actual observation error of SFA model is mixed error ðuit þ vit Þ, the impact of inefficiency should be separated. Based on the previous decomposition method, the energy input efficiency and energy conservation potential can be expressed respectively as Ef f it ¼

j

i

ð2Þ

ia j

i

If we replace the input X i with energy input Eit , capital input K it and labor input Lit , with the assumption of linearly homogenous in energy input, we can rewrite Eq. (1) as  Ln Eit ¼ Fð U Þ  Ln DI Introduce the statistical Ln Dit' ¼ uit ; uit Z 0.

error

vt

into

Eq.

(3).

Ln Eit ¼ Fð UÞ þuit þ vit

ð8Þ

uit ¼ zit δ þ εit ;

uit 4 0

uit  i:i:d: 1Nðzit δ; s2u Þ;

ð9Þ

εit  i:i:d: Nð  zit δ; s2u Þ vit  i:i:d: Nð0; s2v Þ

uit is explained by a linear regression model Eq. (9), zit is a vector made up by inefficient explanatory variables, δ is the parameter vector (Battese and Coelli, 1995).

ð3Þ

4. Variables and data source

Let

This paper uses provincial panel data to analyze the energy input efficiency and energy conservation potential of China's iron and steel industry. As the energy data of some provinces are missing seriously, the samples are made up of 26 provinces' panel data of the iron and steel industry between 2005 and 2011. The main data come from ‘China Iron and Steel Industry Yearbook, 2006–2012’, ‘China Industry Economy Statistical Yearbook, 2006– 2012’, ‘China Statistical Yearbook, 2006–2012’ and Statistical yearbook of each province. Some data of Shanghai come from Shanghai Industrial Energy Efficiency Guide (2011). All economic data of each province are converted to the 2005 price level and logarithm. Main variables are listed in Table 2.

ð4Þ

Eq. (4) is the basic form of stochastic frontier function that Aigner et al. (1977) proposed. Eq. (4) can be expressed as Ln Eαit ¼ β0 þ βy Ln Y it þ βk Ln K it þ βl Ln Lit þ 12 βyy ðLn Y it Þ2 þ 12 βyy ðLn K it Þ2 þ 12 βyy ðLn Lit Þ2 þ βkl Ln K it n Ln Lit þβyk Ln Y it n Ln K it þ βyl Ln Y it n Ln Lit þ βt T þ 12 βtt T 2 þβty T Ln Y it þ βtk T Ln K it þ βtl T Ln Lit þuit þ vit

ð7Þ

In Eq. (7), Eð U Þ is conditional expectation, Ef f it is the energy input efficiency. Eq. (8) is the energy conservation potential. According to Eq. (6):

where X i is input factor, Y is output. In addition to meetingLn DI Z0, Eq. (1) should also satisfy the following conditions: βij ¼ βji ;

EðEit juit ¼ 0; Y it ; K it ; Lit ; Z it Þ ¼ expð  uit Þ EðEαit juit a0; Y it ; K it ; Lit ; Z it Þ

CONSERV it ¼ EðEαit Þð1  Ef f it Þ

ð1Þ

∑ βji ¼ ∑ βiy ¼ ∑ βit ¼ 0;

ð6Þ

ð5Þ

Table 2 Variables description. Variables

Short

Sample size

Mean

Standard deviation

Min

Max

Production variables Industrial output current price (100 million) Energy input (104 tce) Capital input (100 million) Labor input (104)

Y E K L

182 182 182 182

1185.959 1748.872 438.492 9.960

1519.007 1761.396 518.935 10.333

31.46 201.042 13.011 1.32

11,068.99 9910.951 3739.031 54.72

Non-efficiency variables Industry concentration Ownership Structure

CONR SOE

182 182

4.516 0.432

2.967 0.234

0.225 0.010

20.586 0.930

Regional variables Eastern China North Eastern China Central China Western China

D1 D2 D3 D4

Beijing, Tianjin, Hebei, Shanghai, Shandong, Fujian, Guangdong Heilongjiang, Liaoning, Jilin Shanxi, Anhui, Henan, Jiangxi, Hubei, Hunan Inner Mongolia, Shannxi, Chongqing, Gansu, Qinghai, Ningxia, Xinjiang, Guangxi, Guizhou, Yunnan

B. Lin, X. Wang / Energy Policy 72 (2014) 87–96

91

Electricity

3.600 MJ/ (860 kcal)/kWh 0.1229 kgce/kWh

Natural gas

3.893 MJ/ (9310 kcal)/ m3 1.3300 kgce/m3

Fuel oil

41.816 MJ/ (10,000 kcal)/kg 1.4286 kgce/kg

4.1. Industrial output (Y) Taking energy input into account, we use data of the industrial gross value of China’s iron and steel sector as output. Data are from Statistical Yearbook of each province. In order to eliminate the impact of the price factor, we convert the data to the 2005 price level by deflating metallurgical industrial product price index. 4.2. Energy input (E) We use the total energy consumption (coal, coke, oil etc.) to represent energy input. Energy input data also come from Statistical yearbook of each province. The default individual data was obtained using linear interpolation. Unit of each primary energy resource was converted to Mtce, and the converting coefficients are shown in Table 3. When converting the electricity unit to coal equivalent, we use the energy calorific value to calculate, and the coefficient is 0.1229 kg of coal equivalents/kWh.

42.652 MJ/ (10,200 kcal)/kg 1.4571 kgce/kg 43.070 MJ/ (10,300 kcal)/kg 1.4714 kgce/kg 43.070 MJ/ (10,300 kcal)/kg 1.4714 kgce/kg 28.435 MJ/ (6800 kcal)/kg 0.9714 kgce/kg Average low calorific Value Conversion factor

20.908 MJ/ (5000 kcal)/kg 0.7143 kgce/kg

41.816 MJ/ (10,000 kcal)/kg 1.4286 kgce/kg

Kerosene Coke Coal

Table 3 Conversion factors from physical unit to coal equivalent. Source: China Energy Statistical Yearbook (2012).

Crude oil

Gasoline

Diesel

4.3. Capital input (K) There are two statistics of fixed capital in China. One is original fixed asset and the other is net fixed asset. The net fixed asset is the original value minus accumulated depreciation. Both statistics use the historical purchase value to represent the value of capital goods, which are not good reflection of the real capital stock (Huang et al., 2002). Many researchers estimated the total capital stock in China, such as He (1992), Chow (1993), Ren and Liu (1997), Zhang (2002), He et al. (2003), Zhang and Zhang (2003), Zhang et al. (2004) and so on. However, no research focus on the capital stock in China's iron and steel industry. Therefore, we apply the perpetual inventory method (PIM) to calculate the capital stock. The PIM was first applied by Goldsmith (1951). Since then it is widely used in calculation of capital stock. Its core assumption is use of geometric pattern of declining relative efficiency, and the replacement rate is a constant. It can be expressed as follows: K t ¼ K t  1 ð1  δt Þ þ I t

ð10Þ

where K t represents capital stock in year t, K t  1 represents capital stock in year t  1; I t represents investment in year t; and δt represents depreciation rate in year t. Therefore, there are four variables for calculating capital stock: (1) Capital stock for the base year: Like Zhang et al. (2004), we use net fixed assets of iron and steel industry of 2005 as capital stock for the base year. It can be obtained directly from Statistical Yearbook. Thus, we apply PIM to estimate the capital stock of each provincial iron and steel industry between 2005 and 2011. (2) Investment during each year: We adopt the method of Wang (2004) and Shan (2008) that investment series data (I t ) equals to the fixed assets in year t minus the fixed assets in year t 1 to obtain the investment data of each year. (3) Price index of fixed-asset investment: We can obtain the data directly from the China Statistical Yearbook, 2006–2012. (4) Depreciation rate: We adopt the method of Zhang et al. (2004) to estimate and obtain the depreciation rate. Under the assumption of the relative efficiency exponentially decreasing, the depreciation rate of China's iron and steel industry is 9.6%. 4.4. Labor input (L) Labor is another important input in iron and steel production. In this paper, we use the number of employees as a measure of labor input indicator. Data come from China Industry Economy Statistical Yearbook, 2006–2012. The employment data in the Yearbook contains two scopes: the average annual value and the value at the end of the year. We adjusted the values into average annual values by calculating the average value of the adjacent two

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B. Lin, X. Wang / Energy Policy 72 (2014) 87–96

Table 4 Final results of the SFA model. Coefficient

Variable

Coefficient value

T value

Coefficient

Variable

Coefficient value

T value

β0 β1 β2 β3 β4 β5 β6 β7 β8 β9 β10 β11

Constant Ln Y Ln K Ln L 1/2 Ln Y Ln Y 1/2 Ln K Ln K 1/2 Ln L Ln L Ln KnLn L Ln YnLn K Ln YnLn L T 1/2T2

11.052nn  1.943nn  0.385 2.547nn 0.202 0.153nn 0.697nn  0.471nn 0.123  0.085 0.052  0.007

15.427  3.563  0.859 6.477 0.882 4.020 4.795  2.609 0.896  0.609 1.211  0.650

β12 β13 β14 β15 β16 β17 δ1 δ2

TnLn Y TnLn K TnLn L D1 D2 D3 CONR SOE

γ Log-likelihood LR_test

0.058  0.065  0.022  0.715nn 0.090  0.0.391nn  0.815nn 0.628nn 1.076 0.999

3.281  3.172  0.862  15.085 1.892  6.637  5.539 4.555 9.460 861,038.88

 33.198 93.584

nn

s2

Means significant at 5%.

years. For some missing data, we obtain them by computing overall labor productivity multiplied by the industrial value added.

Table 5 Energy efficiency and energy conservation potential in China’s iron and steel industry (2005–2011).

4.5. Regional variable (D) The development level of iron and steel industry in different parts of China differs significantly. In order to estimate the energy efficiency accurately, this paper divides China into Eastern China, Northeastern China, Central China and Western China for this study. The regions are divided according to the classification of China Statistic Yearbook. 4.6. Inefficient explanatory variables According to the literature, in this paper, we select the following variables as inefficient explanatory variables. Industry concentration (CONR): We use the average industry output to represent the concentration. This is industrial gross value divided by the number of iron and steel enterprises in each province. In general, for a fully competitive market, the more the number of enterprises, the smaller the average industrial output. This means that the industry is more decentralized and competitive. However, larger average industrial output always means a higher degree of market monopoly. Therefore, this indicator, to some extent, reflects the competitiveness of the industry. The large scale enterprises are always more mature, and they put more investments in technology to promote energy saving. However, if there are many small enterprises in the industry, competition would be more severe as enterprises will be more interested in expanding production than saving energy. This paper assumes that this indicator has negative impact on inefficiency. Ownership structure (SOE): We use the proportion of the stateowned employees in total employees to represent the ownership structure. State-owned enterprises are characterized by heavy policy burden, ill-defined property rights and easier access to national policy support and financial subsidies compared to private enterprises. The resulting soft budget constraint and the lack of an effective incentive mechanism are the main reasons for inefficiency in state-owned enterprises (He, 2011). The more the number of state-owned enterprises, the more severe the situation of energy input in this industry. This paper uses this indicator as an inefficient explanatory variable, and assumes a positive correlation between the two.

5. Empirical results 5.1. Results of the SFA model Through test of the SFA model function form, we can obtain a stochastic frontier model including energy input. The certain

Energy efficiency

Energy conservation potential (Mtce)

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangxi Guangdong Chongqing Guizhou Yunnan Shannxi Gansu Qinghai Ningxia Xinjiang

0.729 0.792 0.932 0.631 0.821 0.867 0.947 0.756 0.904 0.797 0.388 0.731 0.549 0.431 0.217 0.840 0.463 0.870 0.415 0.473 0.836 0.720 0.913 0.780 0.533 0.851

10.803 18.736 37.678 82.587 28.125 46.322 3.824 6.225 19.314 20.245 25.845 22.921 122.458 56.745 44.298 21.076 43.674 14.107 16.187 31.695 15.069 8.781 6.337 6.317 8.178 5.893

Average efficiency Cumulative energy conservation potential

0.699 –

– 723.439

frontier function is a trans-log model which contains non-neutral technical progress. Table 4 shows the final estimation results of the SFA model. Although estimation results of several parameters are not significant, the null hypothesis γ¼0 is rejected and the model is still valid and can be used to explain the energy input efficiency in China's iron and steel industry. The estimated coefficients in the inefficient variables are of particular interest to this study. The negative coefficient of CONR ( 0.815) means that enlarging the production scale is conducive for energy efficiency. The large-scale steel enterprises are more efficient in energy input. The positive coefficient of SOE (0.628) means that the higher the degree of nationalization of steel enterprises, the less the efficient energy input in the production process. Both of the two inefficient variables are significant. This indicates that structural defect in China's economy is still the main influencing factor that hinders economic growth. In the past 30 years, although the proportion of state-owned enterprises in

B. Lin, X. Wang / Energy Policy 72 (2014) 87–96

140

0.8 0.75 0.7 0.65 0.6 0.55 0.5 0.45 0.4 0.35 0.3

120 Mtce

China's iron and steel sector declined continuously, it still maintained a high ratio. State-owned enterprises are often subject to government intervention and have access to a variety of policies for protection and subsidies. The direct consequence of these protection and subsidies is that it weakens the enthusiasm of enterprises to improve efficiency. In contrast, private and foreign enterprises face severe budget constraints, and as a result must maximize cost effectiveness and energy efficiency.

93

100 80 60 40 2005

2006

2007

2008

2009

Energy conservation potential

5.2. Energy conservation potential in China's iron and steel industry

2010

2011

Energy efficiency

Fig. 3. Changes of energy efficiency and energy conservation potential (2005–2011).

According to the estimates of the stochastic frontier model, we can evaluate energy input efficiency and energy conservation potential in China's iron and steel industry (see Table 5). Input efficiency represents the ratio of effective energy input and the actual energy input at the given level of output. Efficiency o 1 indicates the presence of excessive input. Energy conservation potential indicates energy saving in the steel industry by improving technical efficiency and getting closer to the frontier. The results show that during 2005–2011, the average inter-provincial energy efficiency of China's iron and steel industry is 0.699, and the excess energy input is 31.1%, resulting in cumulative energy conservation potential of 723.44 Mtce in 7 years. The average energy conservation potential was 103.35 Mtce for one year. Lin et al. (2011) estimated the single factor energy efficiency and the energy conservation potential of China's steel industry using the linear regression model. The results show that energy conservation potential will be 130 Mtce under scenario A and 50 Mtce under scenario B in 2015. This paper is different compared to the previous research. Essentially, the concept of energy conservation potential is a comparison between the actual energy input and the effective energy input. The main difference in the two studies lies in the selection of the effective energy input. The former research chooses Japan's energy input because the energy efficiency in Japan's iron and steel industry is the highest in the world. This paper chooses the most effective energy input considering the production capacity of China's iron and steel industry. Furthermore, the former one is based on the single factor energy efficiency. In this paper, considering the impact of both the production factors and inefficiency factors, we analyze the totalfactor energy efficiency, and then evaluate the maximum energy conservation potential. According to changes in energy efficiency, we calculated the average input efficiency of the 26 provinces. As it can be seen from Fig. 3, the energy efficiency of China's iron and steel industry shows some level of fluctuation, increasing from 0.608 in 2005 to 0.721 in 2011. It is noteworthy that the energy efficiency in 2008 was at a low point, which reflected the fact that China's iron and steel industry experienced a transformational change from rapid growth to ‘cold winter' in 2008. In 2008, China's iron and steel production changed from high-speed growth to a negative growth, and the steel market changed from a buyer's market to seller's market due to the resource shortage. However, energy consumption over the years still increased, resulting in low energy efficiency. With the low energy input efficiency of China's iron and steel industry during the 11th Five-Year period, the average annual energy conservation potential remained at about 100 Mtce. 5.3. Analysis of provincial differences As shown in Fig. 4, the iron and steel productions in the provinces are different. The average annual crude steel production of Hebei was the highest (about 12 million tons) during 2005– 2011. This was followed by Shandong, Liaoning, Shanxi, Hubei and so on. The crude steel production of Ningxia was the lowest, which was 98 thousand tons. Moreover, Qinghai (1091 thousand tons),

Hebei Shandong Liaoning Shanxi Hubei Shanghai Henan Tianjin Anhui Jiangxi Hunan Inner Mongolia Guangdong Yunnan Guangxi Fujian Jilin Gansu Xinjiang Beijing Heilongjiang Shanxi Chongqing Guizhou Qinghai Ningxia 0

20000

40000

60000

80000

100000 120000

Crude steel (thousand tons) Fig. 4. Annual production of crude steel on average (2005–2011). . Source: The data come from China Iron and Steel Industry Yearbook (2006–2012)

Guizhou (3560 thousand tons) and Chongqing (3943 thousand tons) were also at the bottom, but their production was quite considerable. The annual production of Qinghai was still larger than some European countries, such as Norway and Switzerland, whose production of crude steel was 600 and 1400 thousand tons in 2011 respectively (WSA, 2012). In order to further analyze the differences of the efficiency in China's iron and steel industry in different provinces, we adopted the method of Shi and Chen (2011) to classify the 26 provinces by the two indicators of energy efficiency and energy input. In Shi and Chen (2011), they did not state clearly the criteria of classification. To compare the energy input of China's iron and steel industry, we first divided the provinces into three categories in accordance with energy consumption in steel production. The provinces whose annual energy consumption is between [0, 10] Mtce are classified as low-input group. Those whose annual energy consumption is between [10, 20] Mtce are classified as mid-input group, and those whose annual energy consumption is higher than 20 Mtce are classified as high-input group. Secondly, in each input group, we divided the provinces by their energy efficiency. All 26 provinces were divided into 9 categories based on the two indicators above. As shown in Table 6, we are most concerned about the lowest efficiency categories. Firstly, Shanxi Province is a coal resource-rich province. Its iron and steel enterprises consume large amount of energy, but the total factor energy efficiency is very low, indicating it wastes a lot of energy. Although Shandong is a coastal province, only 20% of the iron and steel productivity layout is in the coastal areas while 80% of the steel productivity capacity is in restricted inland areas surrounding the cities. From the 10th Five-Year period (2001–2005), the iron and steel industry in Shandong began to follow the development path of low-cost expansion, leading to

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Table 6 Classification by energy input and efficiency.

High efficiency Middle efficiency Low efficiency

High input

Middle input

Low input

Shanghai, Hebei Hunan, Inner Mongolia, Liaoning Shanxi, Shandong

Guangdong, Yunnan, Jilin, Gansu Jiangxi, Anhui, Tianjin Henan, Guangxi

Xinjiang Beijing, Shannxi, Heilongjiang, Qinghai, Ningxia Hubei, Fujian, Chongqing, Guizhou

Table 7 Regional differences of energy efficiency.

D1 D2 D3 D4

1.0

2005

2006

2007

2008

2009

2010

2011

Average

0.752 0.686 0.505 0.546

0.823 0.750 0.562 0.669

0.770 0.897 0.613 0.745

0.684 0.900 0.601 0.686

0.722 0.918 0.673 0.703

0.722 0.919 0.648 0.692

0.688 0.926 0.651 0.723

0.737 0.857 0.608 0.681

0.9 0.8 0.7 0.6

rapidly increasing production, but with low energy efficiency. The energy conservation in such provinces should play the most important role in implementing the country's energy conservation plan. Secondly, in Henan and Guangxi province, the annual energy input was more than 10 Mtce in the recent five years, but the energy efficiency had always been at about 0.5. The industries in these provinces are at early development stage, and they need energy to boost growth. However, we should balance between the target of economic growth and energy consumption. Thirdly, the energy efficiency of Jiangxi, Anhui and Tianjin are moderate, but the energy input is between [10, 20] Mtce. The total factor energy efficiency of Anhui province steadily grew from 0.602 in 2005 to 0.966 in 2011, slightly close to the high efficiency group. The total factor energy efficiency of Jiangxi Province and Tianjin City also increased variedly, and in 2011 reached the level of 0.754 and 0.702, respectively. As long as the iron and steel enterprises in this group improve their production techniques and reduce energy waste, they can enter the high efficiency provinces. Thus, there are great potentials to improve energy efficiency in these provinces. 5.4. Analysis of regional differences According to the economic regional classification in Section 4, the total factor energy efficiency of each region is evaluated and analyzed, and the results are shown in Table 7 and in Fig. 5. Overall, the efficiency of Northeastern China (D2) is relatively high. Changes in the total factor energy efficiency in D2 were also relatively flat during 2005–2011. However, energy efficiency of Eastern China (D1) was high in 2005–2006, but showed a downward trend after that. The total factor energy efficiency in Central and Western China (D3 and D4) was lower. In the study interval, the average energy efficiency in Northeastern China was the highest (0.857). The cumulative energy conservation potential was 56.37 Mtce. The energy efficiency also maintained an upward trend since 2005, which was consistent with the revitalization plan of the old industrial base. In 2005, Anshan and Benxi Steel jointly re-organized to saddle the iron and steel group. This was of great significance to the development of iron and steel industry in Northeast China, and greatly improved concentration and enhanced the overall competitiveness of the industry in the Northeast. The average energy efficiency in Eastern China was the second (0.737). The cumulative energy conservation potential was 278.51 Mtce. Although the average energy efficiency of Eastern China was still higher than Central and Western regions, there was a downward trend, which was largely related to the ownership structure of the enterprises. The iron and steel enterprises in Eastern China developed earlier and the proportion of state-

0.5 0.4 2004

2005

Eastern China

2006

2007

2008

Northeastern China

2009

2010

Central China

2011

2012

Western China

Fig. 5. Changes of regional differences of energy efficiency in China's iron and steel industry.

owned enterprises was high. The average number of employees in state-owned and state holding enterprises still accounted for 41.3% of total employees in all steel enterprises. According to the earlier estimates, the higher the proportion of state-owned enterprises, the lower the energy efficiency will be. Therefore, in the future, the focus of the energy efficiency improvement and energy conservation in Eastern China should be the reform of ownership. China's government should encourage private and foreign investment in Eastern steel enterprises. The average energy efficiency in Western and Central China was 0.681 and 0.607 respectively and the cumulative energy conservation potential was 140.69 Mtce and 247.87 Mtce respectively. The energy efficiency in Western and Central China developed like ‘W’ in 2005–2011, fluctuating between 0.5 and 0.7. In recent years, the energy efficiency of the steel enterprises in Western China had developed substantially, driven by the strong national support of the Western Development Plan, especially the support of the iron and steel industry in the western region, technological progress and independent innovation, industrial restructuring and upgrading, changes in development model, large-scale technology and equipment, and automation and modernization transformation. In 2011, China's government also set regional policy guidelines in the ‘12th Five-Year Plan’, and would significantly support the economic development in Western China in the future. Urban construction, transportation and other infrastructures will result in large demands for iron and steel, which will further stimulate the development of the local iron and steel industry and bring opportunities for them to improve energy saving techniques, adjust ownership structure and improve energy efficiency.

6. Conclusion and policy proposals In this paper, energy efficiency is defined as the ratio of the effective input at level of the frontier technology and the actual energy input. Energy conservation potential is defined as energy conservation through elimination of technical inefficiency under

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the premise of the existing frontier technology and the fixed output. The empirical analysis of China's iron and steel industry by the trans-log production function model shows that: (1) during the period of 2005–2011, the average total factor energy efficiency of China's iron and steel industry was 0.699, the cumulative energy conservation potential was 723.44 Mtce, and the average annual energy conservation potential was 103.35 Mtce. (2) Overall, the energy efficiency of China's iron and steel industry increased with some fluctuations from 0.608 in 2005 to 0.721 in 2011, indicating an increase of 10.7%. (3) The inefficient variable of CONR was conducive for energy efficiency improvement while SOE was an important factor hindering the improvement of energy efficiency. (4) Provinces of Shanxi, Shandong, Jiangxi, Anhui, Tianjin, Henan and Guangxi are the focuses of energy conservation and emissions reduction. (5) Energy efficiency was relatively high in Northeastern China, while it was relatively low in Central and Western China. Thus, Central and Western China have the greatest potential for energy conservation. By improving the efficiency of the frontier, the potential can be realized. The decline of energy efficiency in Eastern China caused by ownership drawbacks also needs to be addressed. The specific policy proposals for improving energy conservation in China's iron and steel industry can be summarized as follows: (1) Improve the industrial concentration: First, an important measure is eliminating old energy intensive production capacity, controlling new production capacity, and enhancing the process of restructuring in the industry. According to the “12th Five-Year Plan of Iron and Steel Industry” (Ministry of Industry and Information Technology, 2011), Blast Furnace (BF) steel production under 400 m3, Basic Oxygen Furnace (BOF) and Electric Arc Furnace (EAF) below 30 tons should be reduced. This measure is especially important for Western and Central area, where backward production capacity exists. Along with this policy, many backward enterprises in Shanxi have been reduced. However, the industrial concentration is still very low and needs further effort. The production in Xinjiang is also relatively old. There are 5 million tons of iron making capacity and 3.2 million tons of steelmaking capacity that needs to be eliminated at the end of the 12th Five-Year period. Therefore, energy input in Western and Central China should be reduced so that the steel industry capacity restructuring in various regions will play an important role in the inhibition of the increase in the total energy input. Another way is promoting corporate mergers and acquisitions within the industry. On 22 January 2013, China's government put forward the mergers and acquisitions goals of the steel industry (MIIT et al., 2013), with the industrial concentration target of the top 10 steel companies should be 60% in 2015. Mergers and acquisitions are important in developed area such as Shandong and Tianjin. Furthermore, the Chinese government should provide financing supports for large-scale enterprises in upgrading technological equipment. (2) Ownership structure reforms: There is need to break stateowned monopoly through ownership structuring reform so that iron and steel companies will be further constrained to conserve energy inputs. Until now, the iron and steel industry has lagged behind the other industries in China's manufacturing sector in terms of ownership adjustment. This is because the state investment was still the most important force for investment. By 2010, the asset of state-owned and state holding enterprises still account for 54.5% of the total assets of large-scale steel enterprises. The empirical result shows that SOR coefficient is positive (0.628). Therefore, further deepening of the ownership reform and reducing state-owned

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proportion is an important means of realizing energy conservation in the industry. As mentioned earlier, high stateowned proportion is the main reason for downward energy efficiency of Eastern China. So this measure can bring new incentive of energy conservation to mature enterprises in Eastern area. (3) Strengthen technical innovation and energy conservation investment: The utilization of energy-saving technology is the basis of energy conservation. According to the guidance of “12th Five-Year Plan of Iron and Steel Industry” (MIIT, 2011) and the “Key Promoting Energy-saving Technology Directory” (NDRC, 2011), innovation investments need to be strengthened in the steel industry. However, the awareness of energy conservation is still weak. The government should make more efforts in promoting energy conservation investment. Meanwhile, technical innovation should also be combined with the elimination of backwardness, mergers and acquisitions, and supplemented by overall regional planning. It is noteworthy that utilization of energy-saving technology depends on its costs and energy prices. New energy-saving technique can be adopted only when the technical cost is below the benefit of energy conservation. Energy conservation choice for enterprises also depends on the balance of the higher initial energy-saving investment costs and future uncertain energy costs (Kenneth et al., 2009). (4) Market-oriented reform: As energy prices play important role in energy saving, the fundamental solution to energy conservation in China lies on market-oriented reform of energy prices. The excessive use of energy in China's industries was due to the state control of energy price. Through energy price reform, industrial enterprises will obtain the correct information on factor prices to enlarge energy conservation investment and promote the application of energy saving technologies. Furthermore, industrial restructuring will be more effective with the market-oriented price, which would ultimately benefit energy conservation. (5) Other policies and measures: Energy substitution is another important way of energy conservation and carbon mitigation. As the proportion of electricity is gradually increasing in iron and steel production, enterprises should continue to adjust energy structure, increase the proportion of clean power and replace coal and other fossil fuels with renewable resources. This is especially useful for enterprises in Western China, such as Gansu and Guizhou, where hydro-power is abundant. When renewable energy and nuclear energy become the mainstream, the production of iron and steel would enter lowcarbon steel metallurgy. From the perspective of taxation, the implementation of resource tax can restrict the excessive use of energy and help energy conservation. According to the “Implementation Regulation of Resource Tax (2011)”, coal, coke, oil and natural gas should be taxed differently. Furthermore, as the import of iron ore is relatively high in China, increasing domestic exploration of iron ore, and accelerating the recycling of scrap steel will promote the development of China's iron and steel industry and energy conservation efforts.

Acknowledgements The paper is supported by Newhuadu Business School Research Fund, the China Sustainable Energy Program (G-1311-19436), National Natural Science Foundation of China (Grant No. 71203186), National Natural Science Foundation of China (Grant No. 71203187) and Ministry of Education (Grant No. 10JBG013).

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